Emission legislation for passenger cars has become more stringent and the increasing demand for reduced fuel consumption has resulted in the introduction of complex new engine and after-treatment technologies involving significantly more control parameters. Vehicle manufacturers employ a time consuming engine parameter calibration process to optimise vehicle performance through the development of engine management system control maps. The traditional static calibration methods require an exponential increase in calibration time with additional calibration parameters and control objectives. To address this issue, this thesis develops and investigates a novel Inverse Optimal Behaviour Based Dynamic Calibration methodology and its application to diesel engines. This multi-stage methodology is based on dynamic black-box modelling and dynamic system optimisation. Firstly the engine behaviour is characterized by black-box models, based on data obtained in a rapid data collection process, for accurate dynamic representation of a subject engine. Then constrained dynamic optimisation is employed to find the optimal input-output behaviour. Finally the optimal input-output behaviour is used to identify feedforward dynamic controllers. The current study applies the methodology to an industrial state-of-the-art WAVERT model of a 1.5 litre Turbo EU6.1 Diesel engine acting as a virtual engine. The approach directly yields a feedforward controller in a nonlinear polynomial structure which can either be directly implemented in the engine-management system or converted to a dynamic or static look-up table format. The results indicate that the methodology is superior to the conventional static calibration approach in both computing efficiency and control performance. A low-cost Transient Testing Platform is presented in this work to carry out transient data collection experiments on a steady-state dynamometer with application to non-linear engine and emissions modelling using State Space Neural Networks. This modelling technique is shown to be superior to the polynomial models and achieves similar performance to non-linear autoregressive with exogenous input neural (NARMAX) network models. Numerical Dynamic Programming is investigated in a simplified engine calibration problem for a virtual engine to potentially improve the dynamic calibration optimisation stage. In a second study the novel dynamic calibration methodology is applied to the airpath control of a 3.0L Jaguar Land Rover (JLR) turbocharged Diesel engine utilizing a direct optimisation approach and State Space Neural Network models. A complete experimental application of the methodology is demonstrated in a vehicle where the vehicle-implemented calibration is obtained in a one-shot process solely from data obtained from the fast dynamic dynamometer testing. The results obtained demonstrate the potential of this methodology for the rapid development of efficient dynamic feedforward controllers based on limited data from the engine test bed.